Genetic algorithm attributes for component selection

This paper uses a genetic algorithm for component selection given a user-defined system layout, a database of components, and a defined set of design specifications. A genetic algorithm is a search method based on the principles of natural selection. An introduction to genetic algorithms is presented, and genetic algorithm attributes that are useful for component selection are explored. A comparison of these attributes is performed using two industrial design problems. A set of genetic algorithm attributes including integer coding, uniform crossover, anti-incest mating, variable mating and mutation rates, retention of population members from generation to generation, and an attention shifted penalty function are suggested for a more efficient search in component selection problems.

[1]  J. Vogwell Computer-aided component selection: a new and expanding research activity , 1990, Comput. Aided Des..

[2]  Susan Elizabeth Carlson,et al.  Component selection optimization using genetic algorithms , 1993 .

[3]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[4]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[5]  Don R. Brown,et al.  Solving fixed configuration problems with genetic search , 1993 .

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  Zbigniew Michalewicz,et al.  Handling Constraints in Genetic Algorithms , 1991, ICGA.

[8]  James F. Thorpe,et al.  Mechanical system components , 1989 .

[9]  S. E. Carlson,et al.  Comparison of Three Non-derivative Optimization Methods with a Genetic Algorithm for Component Selection , 1994 .

[10]  Jim Antonisse,et al.  A New Interpretation of Schema Notation that Overtums the Binary Encoding Constraint , 1989, ICGA.

[11]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[12]  Colin R. Reeves,et al.  Using Genetic Algorithms with Small Populations , 1993, ICGA.

[13]  Larry J. Eshelman,et al.  The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , 1990, FOGA.

[14]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[15]  C.-L. Lee,et al.  General framework for configuration design: Part 1—methodology , 1993 .

[16]  John Maynard Smith,et al.  The Theory of Evolution , 1958 .

[17]  Warren P. Seering,et al.  The Performance of a Mechanical Design `Compiler'' , 1989 .

[18]  Alice M. Agogino,et al.  An Intelligent Real Time Design Methodology for Component Selection: An Approach to Managing Uncertainty , 1994 .

[19]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[20]  Peter T. Cummings,et al.  Algorithmic efficiency of simulated annealing for heat exchanger network design , 1990 .

[21]  Larry J. Eshelman,et al.  Biases in the Crossover Landscape , 1989, ICGA.